Management of sleep disorder by preceptors in a family medicine residency program in Calgary, Alberta: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Most prescriptions for sedative-hypnotics are written by family physicians. Given the influence of preceptors on residents' prescribing, this study explored how family physician preceptors manage sleeping problems. METHODS: Family physician preceptors affiliated with a postgraduate training program in Alberta were invited to participate in this mixed-methods study, conducted from January to October 2021. It included a quantitative survey of preceptors' attitudes to treatment options for sleep disorder, perceptions of patient expectations and self-efficacy beliefs. Participants indicated their responses on a 5-point Likert scale ranging from "strongly disagree" to "strongly agree." Respondents were then asked whether they were interested in participating in a semistructured qualitative interview that elicited preceptors' management of sleep disorder in response to a series of vignettes. We analyzed the quantitative data using descriptive statistics and the qualitative interviews using thematic analysis. RESULTS: Of the 76 preceptors invited to participate, 47 (62%) completed the survey, and 10 were interviewed. Thirty-two survey respondents (68%) were in academic teaching clinics, and 15 (32%) were from community clinics. The majority of participants (34 [72%]) agreed they had sufficient expertise to use nondrug treatment. Most (43 [91%]) had made efforts to reduce prescribing, and 45 (96%) felt able to support patients empathically when not using sleeping medication. The qualitative data showed that management of sleeping disorder was emotionally challenging. Participants hesitated to prescribe sedatives and reported "exceptions" to prescribing, many of which included indications within guideline recommendations. Participants were reluctant to change a colleague's management. INTERPRETATION: Preceptors were confident using nonpharmacologic management to treat sleep disorder and hesitant to use sedative-hypnotics, presenting legitimate use of sedatives as exceptional behaviour. Acknowledging social norms and affective aspects involved in prescribing may support balanced prescribing of sedative-hypnotics for sleep disorder and reduce physician anxiety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".